code: def __init__(
      self,
      inplanes,
      planes,
      stride=1,
      downsample=None,
      dilation=(1, 1),
      residual=True,
  ):
      super(Bottleneck, self).__init__()
      self.conv1 = nn.Conv2d(
          inplanes, planes, kernel_size=1, bias=False
      )
      self.bn1 = BatchNorm(planes)
      self.conv2 = nn.Conv2d(
          planes,
          planes,
          kernel_size=3,
          stride=stride,
          padding=dilation[1],
          bias=False,
          dilation=dilation[1],
      )
      self.bn2 = BatchNorm(planes)
      self.conv3 = nn.Conv2d(
          planes, planes * 4, kernel_size=1, bias=False
      )
      self.bn3 = BatchNorm(planes * 4)
      self.relu = nn.ReLU(inplace=True)
      self.downsample = downsample
      self.stride = stride
  
imports: import torch.nn
  import math
  import torch.utils.model_zoo
label_desc: Use image node_id 1 to create a new Bottleneck object from inherited base classes: nn with example: obj = Bottleneck(inplanes, planes, stride, downsample, dilation, residual)